Lifespan study: SURF1 & Hypoxia
1 Preparation
library(tidyverse)
library(ggpubr)
source("Themes.R")
source("Functions.R")
### Select Seahorse dates to include in the analysis
Seahorse_dates <- c("2020-10-31", #SH2
"2020-11-05", #SH3
"2020-11-10", #SH4
"2020-11-19", #SH5
"2020-11-24", #SH6
"2020-12-03", #SH7
"2020-12-17", #SH9
"2020-12-22", #SH10
"2020-12-29", #SH11
"2021-01-05", #SH12
"2021-01-12", #SH13
"2021-01-26", #SH14
"2021-02-03", #SH15
"2021-02-18", #SH16
"2021-03-18", #SH17
"2021-04-15", #SH18
"2021-05-12",
"2021-06-10") #SH19
Seahorse_Experiment_Names <- c("SH2-Plate1",
"SH2-Plate2",
"SH3-Plate1",
"SH4-Plate1",
"SH4-Plate2",
"SH5-Plate1",
"SH5-Plate2",
"SH6",
"SH7",
"SH9",
"SH10",
"SH11",
"SH12",
"SH13",
"SH14",
"SH15",
"SH16",
"SH17",
"SH18",
"SH19",
"SH20")
Seahorse_names <- c("Baseline_Respiration",
"NonMitochondrial_Respiration",
"Basal_Respiration",
"Max_Respiration",
"Spare_Respiration",
"Proton_Leak",
"ATPlinked_Respiration",
"Coupling_Efficiency",
"Baseline_ECAR",
"Max_ECAR",
"Spare_ECAR",
"ATPtotal",
"ATPglyc",
"ATPox",
"ATPtotal_max",
"ATPglyc_max",
"ATPox_max",
"ATPtotal_spare",
"ATPox_spare",
"ATPglyc_spare",
"Resting_Metabolic_Rate",
"Max_Metabolic_Rate", #"PicoWatts", "Max_PicoWatts",
"OCRcoupled")
# Remove hfB6 2020-11-18\
# Because already contaminated\
# From 2020-11-23 on hFB6 & hFB7 Hypoxia and normoxia excluded due to contamination\
#source(here::here("Scripts", "2020-10-29_Cell_Count_to_Lifespan.R"))
#source(here::here("Scripts","2020-10-30-Seahorse_Data_Analysis.R"))
#source(here::here("ASM_01","Functions.R"))
#### Read the lifespan document created in "2020-10-29_Cell_Count_to_Lifespan.R"
data_Lifespan <- read.csv(here::here("4_Processed_Datasets","data_Lifespan.csv")) %>%
select(-c(X, Hour, Exp_Start, Split_date_time, New_Plated, Hypoxia_vs_Normoxia_Start,
Hours_per_Passage, Days_per_Passage)) %>%
mutate(Date_of_Seahorse = as.Date(Date_of_Seahorse)) %>%
mutate(Date = as.Date(Date))
#data_Lifespan <- data_Lifespan[1:130,]
#### Read the lifespan document created in "22020-10-30-Seahorse_Data_Analysis.R"
data_Seahorse<- list.files(here::here("10_Data_Seahorse_Final"), pattern = "*.csv", full.names = T) %>%
lapply(read_csv) %>%
bind_rows %>%
separate(X1, into=c("Cell_Line","Condition", "Percent_Oxygen")) %>% select(-contains("PPR")) %>%
filter(!(Cell_Line %in% "hFB6"&Date_of_Seahorse == "2020-11-19")) %>%
filter(!(Cell_Line %in% "hFB6"&Date_of_Seahorse == "2020-11-23")) %>%
filter(!(Cell_Line %in% "hFB7"& Date_of_Seahorse == "2020-11-23")) %>%
filter(Experiment_Name %in% Seahorse_Experiment_Names) %>%
mutate(Percent_Oxygen = as.numeric(Percent_Oxygen))
data_combined <- data_Lifespan %>%
full_join(data_Seahorse, by=c("Cell_Line", "Date_of_Seahorse",
"Percent_Oxygen", "Condition")) %>%
mutate(CellVol = (4/3) * pi * (Av_Cell_Size_um/2)^3) %>%
unite(Condition_Ox, Condition, Percent_Oxygen, remove = F) %>%
unite(Cell_Line_Ox, Cell_Line, Percent_Oxygen, remove = F) %>%
mutate(Date_of_Seahorse = as.character(Date_of_Seahorse)) %>%
mutate(Condition_Ox = as.factor(Condition_Ox)) %>%
mutate(Cell_Line_Ox = as.factor(Cell_Line_Ox)) %>%
mutate(Percent_Oxygen = as.factor(Percent_Oxygen)) #%>%
#select(-Date_of_Seahorse)
#### Separate independent and dependent variables
independent_vars <- c("Cell_Line", "Percent_Oxygen", "Old_Passage",
"Condition", "Condition_Ox", "Date_of_Seahorse",
"Days_Grown","Experiment_Name", "Cell_Line_Ox", "Date")
dependent_vars <- colnames(data_combined)[!colnames(data_combined) %in% c(independent_vars)] 2 Lifespan
2.1 Population Doublings
data_combined %>% separateLines_multi(feature = Population_Doublings, plotlines = "smooth",
DoF = 3)
2.2 Population doubling rate
data_combined %>% separateLines_multi(feature = Population_Doubling_Rate, plotlines = "smooth",
DoF = 3)
data_combined %>% separateLines_multi(feature = Population_Doubling_Rate, plotlines = "connectPoints")
2.3 Cell Volume
2.4 Cell Death
3 Seahorse Bioenergetics - Resting
3.1 ATP total
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPtotal,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPtotal,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPtotal,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = ATPtotal)
3.2 ATP Ox
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPox,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPox,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPox,
plotlines = "connectPoints")
3.3 ATP Glyc
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPglyc,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPglyc,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPglyc,
plotlines = "connectPoints")
3.4 Baseline_Respiration
Baseline_Respiration = OCR in resting state, no drugs added
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = Baseline_Respiration,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Baseline_Respiration,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Baseline_Respiration,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = Baseline_Respiration)
3.5 Basal_Respiration
Basal_Respiration = Baseline_Respiration - NonMito_Respiration (after Rotenone/AntimycinA)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = Basal_Respiration,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Basal_Respiration,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Basal_Respiration,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = Basal_Respiration)
3.6 Baseline_ECAR
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = Baseline_ECAR,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Baseline_ECAR,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Baseline_ECAR,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = Baseline_ECAR)
3.7 Resting metabolic rate
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = Resting_Metabolic_Rate,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Resting_Metabolic_Rate,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Resting_Metabolic_Rate,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = Resting_Metabolic_Rate)
4 Seahorse Bioenergetics - Mito stress test
4.1 ATPtotal_max
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPtotal_max,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPtotal_max,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPtotal_max,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = ATPtotal_max)
4.2 ATPtotal_spare
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPtotal_spare,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPtotal_spare,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPtotal_spare,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = ATPtotal_spare)
4.3 ATPox_max
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPox_max,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPox_max,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPox_max,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = ATPox_max)
4.4 ATPox_spare
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPox_spare,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPox_spare,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPox_spare,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = ATPox_spare)
4.5 ATPglyc_max
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPglyc_max,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPglyc_max,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPglyc_max,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = ATPglyc_max)
4.6 ATPglyc_spare
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = ATPglyc_spare,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPglyc_spare,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = ATPglyc_spare,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = ATPglyc_spare)
4.7 Proton leak
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = Proton_Leak,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Proton_Leak,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Proton_Leak,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = Proton_Leak)
4.8 Coupling_Efficiency
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines(feature = Coupling_Efficiency,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Coupling_Efficiency,
plotlines = "smooth", DoF = 2)
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% separateLines_multi(feature = Coupling_Efficiency,
plotlines = "connectPoints")
data_combined %>% filter(Date_of_Seahorse %in% Seahorse_dates) %>% boxplot_multi(feature = Coupling_Efficiency)